To perform this experiment, we divide the dataset into a standard 90%/10% training/evaluation split and then train a U-Net with pixel-level supervision trained to match the ground truth labels [1]}. We start with giving it 100% of the labels, and then gradually introduce more and more noise in the form of multiplying the training labels by a salt-and-pepper vector of 0s and 1s pulled from a binomial with decreasing probability of a 1 coming up. As shown in Figure REF , the accuracy of the supervised model drastically decreases with fewer un-noised supervised labels passing through.
